EDA
Jaimy Lai
2022-12-14
load("~/Downloads/bookings_train.RData")
library("tidymodels")## ── Attaching packages ────────────────────────────────────── tidymodels 1.0.0 ──## ✔ broom 1.0.0 ✔ recipes 1.0.2
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## • Search for functions across packages at https://www.tidymodels.org/find/library("themis")
library("knitr")
library("ranger")
library("doParallel")## Loading required package: foreach##
## Attaching package: 'foreach'## The following objects are masked from 'package:purrr':
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## accumulate, when## Loading required package: iterators## Loading required package: parallellibrary("vip")##
## Attaching package: 'vip'## The following object is masked from 'package:utils':
##
## vilibrary("stargazer")##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerlibrary("GGally")## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library("skimr")
library("corrplot")## corrplot 0.92 loadedlibrary("ggridges")
library("forcats")SKIM
Important notes: - 26 variables - 24035 datapoints - type - char 4 - factor 5 - numeric 17 - no missing values
bookings_train |> skim()| Name | bookings_train |
| Number of rows | 24035 |
| Number of columns | 26 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| factor | 5 |
| numeric | 17 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| arrival_date_month | 0 | 1 | 3 | 9 | 0 | 12 | 0 |
| country | 0 | 1 | 2 | 4 | 0 | 115 | 0 |
| reserved_room_type | 0 | 1 | 1 | 1 | 0 | 10 | 0 |
| assigned_room_type | 0 | 1 | 1 | 1 | 0 | 10 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| is_cancelled | 0 | 1 | FALSE | 2 | no: 17362, yes: 6673 |
| meal | 0 | 1 | FALSE | 5 | BB: 18062, HB: 4771, Und: 703, FB: 449 |
| market_segment | 0 | 1 | FALSE | 5 | Onl: 10688, Off: 4487, Dir: 3888, Gro: 3479 |
| deposit_type | 0 | 1 | FALSE | 3 | No : 22946, Non: 1014, Ref: 75 |
| customer_type | 0 | 1 | FALSE | 4 | Tra: 18151, Tra: 4662, Con: 1043, Gro: 179 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| lead_time | 0 | 1 | 92.74 | 97.60 | 0.00 | 10 | 57 | 155.00 | 737 | ▇▂▁▁▁ |
| arrival_date_year | 0 | 1 | 2016.12 | 0.72 | 2015.00 | 2016 | 2016 | 2017.00 | 2017 | ▃▁▇▁▆ |
| arrival_date_week_number | 0 | 1 | 27.12 | 13.97 | 1.00 | 16 | 28 | 38.00 | 53 | ▆▇▇▇▆ |
| arrival_date_day_of_month | 0 | 1 | 15.79 | 8.89 | 1.00 | 8 | 16 | 24.00 | 31 | ▇▇▇▆▆ |
| stays_in_weekend_nights | 0 | 1 | 1.19 | 1.15 | 0.00 | 0 | 1 | 2.00 | 16 | ▇▁▁▁▁ |
| stays_in_week_nights | 0 | 1 | 3.13 | 2.48 | 0.00 | 1 | 3 | 5.00 | 40 | ▇▁▁▁▁ |
| adults | 0 | 1 | 1.87 | 0.75 | 0.00 | 2 | 2 | 2.00 | 55 | ▇▁▁▁▁ |
| children | 0 | 1 | 0.13 | 0.44 | 0.00 | 0 | 0 | 0.00 | 3 | ▇▁▁▁▁ |
| babies | 0 | 1 | 0.01 | 0.12 | 0.00 | 0 | 0 | 0.00 | 2 | ▇▁▁▁▁ |
| is_repeated_guest | 0 | 1 | 0.04 | 0.21 | 0.00 | 0 | 0 | 0.00 | 1 | ▇▁▁▁▁ |
| previous_cancellations | 0 | 1 | 0.10 | 1.36 | 0.00 | 0 | 0 | 0.00 | 26 | ▇▁▁▁▁ |
| previous_bookings_not_cancelled | 0 | 1 | 0.15 | 1.00 | 0.00 | 0 | 0 | 0.00 | 30 | ▇▁▁▁▁ |
| booking_changes | 0 | 1 | 0.28 | 0.71 | 0.00 | 0 | 0 | 0.00 | 16 | ▇▁▁▁▁ |
| days_in_waiting_list | 0 | 1 | 0.52 | 7.37 | 0.00 | 0 | 0 | 0.00 | 185 | ▇▁▁▁▁ |
| adr | 0 | 1 | 94.58 | 61.17 | -6.38 | 50 | 75 | 124.21 | 450 | ▇▅▂▁▁ |
| required_car_parking_spaces | 0 | 1 | 0.14 | 0.35 | 0.00 | 0 | 0 | 0.00 | 3 | ▇▁▁▁▁ |
| total_of_special_requests | 0 | 1 | 0.62 | 0.81 | 0.00 | 0 | 0 | 1.00 | 5 | ▇▁▁▁▁ |
# Summary stats
stargazer(as.data.frame(bookings_train), type='text')##
## ========================================================================
## Statistic N Mean St. Dev. Min Max
## ------------------------------------------------------------------------
## lead_time 24,035 92.740 97.601 0 737
## arrival_date_year 24,035 2,016.123 0.720 2,015 2,017
## arrival_date_week_number 24,035 27.117 13.968 1 53
## arrival_date_day_of_month 24,035 15.790 8.895 1 31
## stays_in_weekend_nights 24,035 1.195 1.149 0 16
## stays_in_week_nights 24,035 3.132 2.479 0 40
## adults 24,035 1.868 0.754 0 55
## children 24,035 0.129 0.442 0 3
## babies 24,035 0.014 0.120 0 2
## is_repeated_guest 24,035 0.045 0.206 0 1
## previous_cancellations 24,035 0.104 1.357 0 26
## previous_bookings_not_cancelled 24,035 0.148 0.999 0 30
## booking_changes 24,035 0.284 0.707 0 16
## days_in_waiting_list 24,035 0.524 7.370 0 185
## adr 24,035 94.575 61.169 -6.380 450.000
## required_car_parking_spaces 24,035 0.138 0.347 0 3
## total_of_special_requests 24,035 0.618 0.813 0 5
## ------------------------------------------------------------------------# Pairplots
# Subset (only looked good with these vars)
bookings_train_pairplot1 = subset(bookings_train, select = c(is_cancelled, adr, lead_time) )
# pairplot'
options(repr.plot.width = 12, repr.plot.height =16)
ggpairs(bookings_train_pairplot1, binwidth=50)## Warning in warn_if_args_exist(list(...)): Extra arguments: 'binwidth' are being
## ignored. If these are meant to be aesthetics, submit them using the 'mapping'
## variable within ggpairs with ggplot2::aes or ggplot2::aes_string.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
is_cancelled
We have 6673 cancellations within the whole dataset and 17362 non-cancellations.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = is_cancelled))
# Unique vals + count
table(bookings_train$is_cancelled)##
## yes no
## 6673 17362arrival_date_year
Most data is from 2016, then 2017, and then 2015. The order of most to least cancellations is the same. Proportionally, 2017 has the most cancellations, then 2016 and then 2015.
# bookings_train["arrival_date_year"]
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = arrival_date_year))
# Visualisation of cancelled per year
ggplot(bookings_train,
aes(x = arrival_date_year,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per year (conditional probability)
df_arrival_date_year_prob <- bookings_train %>%
group_by(is_cancelled, arrival_date_year) %>%
summarize(n = n()) %>%
group_by(arrival_date_year) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_arrival_date_year_prob, aes(x = arrival_date_year, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing vlaues
sum(is.na(bookings_train$arrival_date_year))## [1] 0# Bizarre values
unique(bookings_train$arrival_date_year)## [1] 2016 2017 2015# Unique vals + count
table(bookings_train$arrival_date_year)##
## 2015 2016 2017
## 4934 11201 7900arrival_date_month
We can observe that around the summer months, there are more bookings, and also the most cancellations. Winter months are more quiet and thus less cancellations due to less bookings. Relatively, proportionally, the summer months july august and september have the most cancellations.
#bookings_train["arrival_date_month"]
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = arrival_date_month))
# Visualisation of cancelled per month
ggplot(bookings_train,
aes(x = arrival_date_month,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per month (conditional probability)
df_arrival_date_month_prob <- bookings_train %>%
group_by(is_cancelled, arrival_date_month) %>%
summarize(n = n()) %>%
group_by(arrival_date_month) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_arrival_date_month_prob, aes(x = arrival_date_month, y = prob, fill = is_cancelled)) +
geom_col() +
scale_x_discrete(limits = c("January","February","March","April","May","June","July","August","September","October","November","December"))
# Check missing vlaues
sum(is.na(bookings_train$arrival_date_month))## [1] 0# Bizarre values
sort(unique(bookings_train$arrival_date_month))## [1] "April" "August" "December" "February" "January" "July"
## [7] "June" "March" "May" "November" "October" "September"# Unique vals + count
table(bookings_train$arrival_date_month)##
## April August December February January July June March
## 2196 2930 1570 1853 1295 2718 1856 2007
## May November October September
## 2155 1460 2129 1866arrival_date_day_of_month
There seems to be no pattern what concerns the dat of cancellation. Logically, day 31 has the least bookings as many months don’t have a 31st day. Day 30 has the most, which is something that cannot be explained by the information given from the assignment.
# bookings_train["arrival_date_day_of_month"]
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = arrival_date_day_of_month))
# Visualisation of cancelled per day of month
ggplot(bookings_train,
aes(x = arrival_date_day_of_month,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per day of month (conditional probability)
df_arrival_date_day_of_month <- bookings_train %>%
group_by(is_cancelled, arrival_date_day_of_month) %>%
summarize(n = n()) %>%
group_by(arrival_date_day_of_month) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_arrival_date_day_of_month, aes(x = arrival_date_day_of_month, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing vlaues
sum(is.na(bookings_train$arrival_date_day_of_month))## [1] 0# Bizarre values
sort(unique(bookings_train$arrival_date_day_of_month))## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31# Unique vals + count
table(bookings_train$arrival_date_day_of_month)##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 769 884 814 764 854 731 727 722 850 724 781 932 662 750 781 859 822 841 772 746
## 21 22 23 24 25 26 27 28 29 30 31
## 706 732 697 815 819 835 748 783 745 872 498arrival_date_week_number
We observe a big spike around the weeks where summer is. Around the beginning and end of the graph you observe the winter months which have less bookings, likely due to many people not being interested in a seaside hotel during the cold winter.
# bookings_train["arrival_date_week_number"]
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = arrival_date_week_number))
# Visualisation of cancelled per arrival_date_week_number
ggplot(bookings_train,
aes(x = arrival_date_week_number,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per arrival_date_week_number (conditional probability)
df_arrival_date_week_number <- bookings_train %>%
group_by(is_cancelled, arrival_date_week_number) %>%
summarize(n = n()) %>%
group_by(arrival_date_week_number) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_arrival_date_week_number, aes(x = arrival_date_week_number, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$arrival_date_week_number))## [1] 0# Bizarre values
sort(unique(bookings_train$arrival_date_week_number))## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53# Unique vals + count
table(bookings_train$arrival_date_week_number)##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 210 276 307 304 291 412 537 444 477 436 401 410 488 431 559 446 516 610 463 470
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 539 545 441 409 433 439 490 583 631 646 635 623 698 668 571 458 396 456 445 410
## 41 42 43 44 45 46 47 48 49 50 51 52 53
## 515 441 555 420 409 319 404 300 428 270 259 348 363##meal Meal seems to show some interesting thinbgs. For instance, we have Undefined and None’s, which along with FB don’t make up a big portion of the data. However, there are more cancellations than not for FB. And for None’s most of the bookings have not cancelled. It could be due to people cancelling FB due to the more premium price it may have, or people are interested in going to other restaurants etc.
# Visualisation of cancelled per meal
ggplot(bookings_train,
aes(x = meal,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per meal (conditional probability)
df_meal <- bookings_train %>%
group_by(is_cancelled, meal) %>%
summarize(n = n()) %>%
group_by(meal) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_meal, aes(x = meal, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$meal))## [1] 0# Bizarre values
sort(unique(bookings_train$meal))## [1] BB HB Undefined FB None
## Levels: BB HB Undefined FB None# Unique vals + count
table(bookings_train$meal)##
## BB HB Undefined FB None
## 18062 4771 703 449 50lead_time
Interestingly, we can observe that cancellations have a higher lead time than the bookings that were not cancelled. Although the non-cancellations have quite a few outliers. This could be due to there simply being more non-cancelled bookings. Anyhow, the graph seems to indicate that a longer lead time means higher chance of cancellations.
boxplot(bookings_train$lead_time~bookings_train$is_cancelled,
xlab="is_cancelled", ylab="Lead Time",
col=topo.colors(3))
legend("bottomleft", inset=.02, title="Cancelled",
c("Yes","No"), fill=topo.colors(3), horiz=TRUE, cex=0.8)
# Minmax
summary(bookings_train$lead_time)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 10.00 57.00 92.74 155.00 737.00stays_in_weekend_nights
There is not a clear pattern when it comes to the amount of stays in the weekend nights.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = stays_in_weekend_nights))
# Visualisation of cancelled per stays_in_weekend_nights
ggplot(bookings_train,
aes(x = stays_in_weekend_nights,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per stays_in_weekend_nights (conditional probability)
df_stays_in_weekend_nights <- bookings_train %>%
group_by(is_cancelled, stays_in_weekend_nights) %>%
summarize(n = n()) %>%
group_by(stays_in_weekend_nights) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_stays_in_weekend_nights, aes(x = stays_in_weekend_nights, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$stays_in_weekend_nights))## [1] 0# Check for bizarre values
sort(unique(bookings_train$stays_in_weekend_nights))## [1] 0 1 2 3 4 5 6 7 8 9 10 12 13 16# Unique vals + count
table(bookings_train$stays_in_weekend_nights)##
## 0 1 2 3 4 5 6 7 8 9 10 12 13 16
## 8433 5597 8361 565 938 21 73 9 26 3 3 3 1 2stays_in_week_nights
As well as for the stays in week nights, there seems to be little pattern.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = stays_in_week_nights))
# Visualisation of cancelled per stays_in_week_nights
ggplot(bookings_train,
aes(x = stays_in_week_nights,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per stays_in_week_nights (conditional probability)
df_stays_in_week_nights <- bookings_train %>%
group_by(is_cancelled, stays_in_week_nights) %>%
summarize(n = n()) %>%
group_by(stays_in_week_nights) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_stays_in_week_nights, aes(x = stays_in_week_nights, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$stays_in_week_nights))## [1] 0# Check for bizarre values
sort(unique(bookings_train$stays_in_week_nights))## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 19 20 21 22 24 25 30 32
## [26] 40# Unique vals + count
table(bookings_train$stays_in_week_nights)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 1660 5478 4375 3554 2048 4648 687 468 299 98 552 18 13 10 13 47
## 16 19 20 21 22 24 25 30 32 40
## 5 25 21 5 1 1 3 3 1 2adults
As for the amount of adults in a booking, it seems as though the more adults, the higher the chance of cancellation. As soon as more than 3 adults book a hotel, the ratio of cancelled and not cancelled is completely red, thus indicating lots of cancellations.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = adults))
# Visualisation of cancelled per adults
ggplot(bookings_train,
aes(x = adults,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per adults (conditional probability)
df_adults <- bookings_train %>%
group_by(is_cancelled, adults) %>%
summarize(n = n()) %>%
group_by(adults) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_adults, aes(x = adults, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$adults))## [1] 0# Check for bizarre values
sort(unique(bookings_train$adults))## [1] 0 1 2 3 4 5 6 10 20 26 27 50 55# Unique vals + count
table(bookings_train$adults)##
## 0 1 2 3 4 5 6 10 20 26 27 50 55
## 9 4330 18803 864 17 1 1 1 2 4 1 1 1children
We can observe that most bookings do not include children. If children are included, it is mostly one or two and very little include 3 children. We can observe that the more children, the more cancellations, although for 3 children the ratio is in favor completely towards no cancellation.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = children))
# Visualisation of cancelled per children
ggplot(bookings_train,
aes(x = children,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per children (conditional probability)
df_children <- bookings_train %>%
group_by(is_cancelled, children) %>%
summarize(n = n()) %>%
group_by(children) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_children, aes(x = children, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$children))## [1] 0# Check for bizarre values
sort(unique(bookings_train$children))## [1] 0 1 2 3# Unique vals + count
table(bookings_train$children)##
## 0 1 2 3
## 21937 1109 984 5babies
Although there is an enormous data imbalance, thus we cannot make an accurate conclusion, it seems as if the more babies, the less cancellations. However, most data has 0 babies.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = babies))
# Visualisation of cancelled per babies
ggplot(bookings_train,
aes(x = babies,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per babies (conditional probability)
df_babies <- bookings_train %>%
group_by(is_cancelled, babies) %>%
summarize(n = n()) %>%
group_by(babies) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_babies, aes(x = babies, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$babies))## [1] 0# Check for bizarre values
sort(unique(bookings_train$babies))## [1] 0 1 2# Unique vals + count
table(bookings_train$babies)##
## 0 1 2
## 23699 331 5country
We can observe that most data comes from portugese and british people, after that spanish and irish. Furthermore, we also have a low of 1’s from different countries.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = country))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = country,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Check missing values
sum(is.na(bookings_train$country))## [1] 0# Check for bizarre values
sort(unique(bookings_train$country))## [1] "AGO" "ALB" "AND" "ARE" "ARG" "ARM" "AUS" "AUT" "AZE" "BEL"
## [11] "BGR" "BHS" "BIH" "BLR" "BRA" "CAF" "CHE" "CHL" "CHN" "CIV"
## [21] "CMR" "CN" "COL" "CPV" "CRI" "CUB" "CYM" "CYP" "CZE" "DEU"
## [31] "DJI" "DNK" "DOM" "DZA" "ECU" "EGY" "ESP" "EST" "FIN" "FJI"
## [41] "FRA" "GBR" "GEO" "GGY" "GIB" "GRC" "HKG" "HRV" "HUN" "IDN"
## [51] "IND" "IRL" "IRN" "ISL" "ISR" "ITA" "JAM" "JEY" "JOR" "JPN"
## [61] "KAZ" "KOR" "KWT" "LBN" "LKA" "LTU" "LUX" "LVA" "MAC" "MAR"
## [71] "MDG" "MDV" "MEX" "MLT" "MOZ" "MUS" "MYS" "NGA" "NLD" "NOR"
## [81] "NPL" "NULL" "NZL" "OMN" "PAK" "PER" "PHL" "POL" "PRI" "PRT"
## [91] "QAT" "ROU" "RUS" "SAU" "SEN" "SGP" "SRB" "SUR" "SVK" "SVN"
## [101] "SWE" "SYC" "TGO" "THA" "TUN" "TUR" "TWN" "UGA" "UKR" "URY"
## [111] "USA" "VEN" "VNM" "ZAF" "ZWE"# Unique vals + count: SORTED
sort(table(bookings_train$country))##
## BGR BHS BIH CRI CYM DJI EGY FJI GGY IDN JAM JOR LKA
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## MAC MDG MLT MUS NPL PER QAT SAU SEN SYC TGO THA TUN
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## UGA VNM ZWE ARM AZE CIV CMR CUB DOM ECU ISL JEY KWT
## 1 1 1 2 2 2 2 2 2 2 2 2 2
## ALB CAF HKG KAZ MEX PAK SGP SUR VEN AND ARE DZA IRN
## 3 3 3 3 3 3 3 3 3 4 4 4 4
## MDV MOZ SRB SVN BLR CPV LBN CYP GRC JPN KOR PRI TWN
## 4 4 4 4 5 5 5 6 6 6 6 6 6
## URY GEO HRV MYS NGA NZL COL GIB SVK OMN PHL TUR CZE
## 6 7 7 7 7 7 8 8 8 10 10 12 13
## CHL AGO EST ISR ZAF UKR IND LVA DNK LTU HUN MAR ARG
## 14 15 15 15 15 16 17 18 27 28 35 38 41
## AUS LUX NOR CHN FIN ROU AUT RUS SWE POL BRA CHE NULL
## 48 49 68 83 86 108 113 120 179 196 252 266 271
## BEL ITA USA NLD CN DEU FRA IRL ESP GBR PRT
## 294 294 297 328 413 738 946 1298 2361 4077 10587market_segment
We can observe that most of the data belongs in the category Online TA, then offline TA/TO, Direct and then Other. Furthermore, we can observe that Direct, Offline TA/TO and Others mostly do not have cancellations, whereas Groups and Online TA have more cancellations.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = market_segment))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = market_segment,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per market_segment (conditional probability)
df_market_segment <- bookings_train %>%
group_by(is_cancelled, market_segment) %>%
summarize(n = n()) %>%
group_by(market_segment) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_market_segment, aes(x = market_segment, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$market_segment))## [1] 0# Check for bizarre values
sort(unique(bookings_train$market_segment))## [1] Direct Groups Offline TA/TO Online TA Other
## Levels: Direct Groups Offline TA/TO Online TA Other# Unique vals + count
sort(table(bookings_train$market_segment))##
## Other Groups Direct Offline TA/TO Online TA
## 1493 3479 3888 4487 10688is_repeated_guest
We can see that most cookings are from first-timers. Howwever, we can also see that the bookings which are a repeated stay, the amount of cancellations in proportionally smaller than for the first-timers.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = is_repeated_guest))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = is_repeated_guest,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per is_repeated_guest (conditional probability)
df_is_repeated_guest <- bookings_train %>%
group_by(is_cancelled, is_repeated_guest) %>%
summarize(n = n()) %>%
group_by(is_repeated_guest) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_is_repeated_guest, aes(x = is_repeated_guest, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$is_repeated_guest))## [1] 0# Check for bizarre values
sort(unique(bookings_train$is_repeated_guest))## [1] 0 1# Unique vals + count
sort(table(bookings_train$is_repeated_guest))##
## 1 0
## 1070 22965previous_cancellations
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = previous_cancellations))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = previous_cancellations,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per previous_cancellations (conditional probability)
df_previous_cancellations <- bookings_train %>%
group_by(is_cancelled, previous_cancellations) %>%
summarize(n = n()) %>%
group_by(previous_cancellations) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_previous_cancellations, aes(x = previous_cancellations, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$previous_cancellations))## [1] 0# Check for bizarre values
sort(unique(bookings_train$previous_cancellations))## [1] 0 1 2 3 4 5 14 19 24 25 26# Unique vals + count
table(bookings_train$previous_cancellations)##
## 0 1 2 3 4 5 14 19 24 25 26
## 23374 545 20 9 3 1 11 11 28 16 17previous_bookings_not_cancelled’
In this graph, we can observe a clear pattern in which, the more bookings people have had in which they did not cancel, the higher the chance that they will not cancel their current booking.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = previous_bookings_not_cancelled))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = previous_bookings_not_cancelled,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per previous_bookings_not_cancelled (conditional probability)
df_previous_bookings_not_cancelled <- bookings_train %>%
group_by(is_cancelled, previous_bookings_not_cancelled) %>%
summarize(n = n()) %>%
group_by(previous_bookings_not_cancelled) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_previous_bookings_not_cancelled, aes(x = previous_bookings_not_cancelled, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$previous_bookings_not_cancelled))## [1] 0# Check for bizarre values
sort(unique(bookings_train$previous_bookings_not_cancelled))## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 25 26
## [26] 27 28 30# Unique vals + count
sort(table(bookings_train$previous_bookings_not_cancelled))##
## 15 19 25 26 27 28 30 17 20 21 22 16 14
## 1 1 1 1 1 1 1 2 2 2 2 3 4
## 18 12 13 11 9 10 8 7 6 5 4 3 2
## 4 5 5 9 13 13 18 25 41 57 77 123 239
## 1 0
## 572 22812reserved_room_type
In this case we see some interesting patterns, most of the reserved rooms are A, then D and then E. We do not have many bookings for P and B but all P’s bookings have been cancelled and none of B’s. However due to the data imbalance, this is a conclusion we cannot make as we lack a proper sample.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = reserved_room_type))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = reserved_room_type,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per reserved_room_type (conditional probability)
df_reserved_room_type <- bookings_train %>%
group_by(is_cancelled, reserved_room_type) %>%
summarize(n = n()) %>%
group_by(reserved_room_type) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_reserved_room_type, aes(x = reserved_room_type, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$reserved_room_type))## [1] 0# Check for bizarre values
sort(unique(bookings_train$reserved_room_type))## [1] "A" "B" "C" "D" "E" "F" "G" "H" "L" "P"# Unique vals + count
sort(table(bookings_train$reserved_room_type))##
## P B L H C F G E D A
## 2 3 4 351 565 625 971 2982 4464 14068assigned_room_type
Once again, here we have a big data imbalance, and therefore cannot make confident conclusions about certain room types. However, interestingly we see that, here, for B we mainly have more bookings not cancelled proportionally, and P only has cancellations when assigned.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = assigned_room_type))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = assigned_room_type,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per assigned_room_type (conditional probability)
df_assigned_room_type <- bookings_train %>%
group_by(is_cancelled, assigned_room_type) %>%
summarize(n = n()) %>%
group_by(assigned_room_type) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_assigned_room_type, aes(x = assigned_room_type, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$assigned_room_type))## [1] 0# Check for bizarre values
sort(unique(bookings_train$assigned_room_type))## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "P"# Unique vals + count
sort(table(bookings_train$assigned_room_type))##
## P B I H F G C E D A
## 2 100 228 414 1002 1111 1353 3393 6216 10216booking_changes
It seems here as though the more bookig changes, the less cancellations. However, it should be remembered that there is data imbalance and most of the data consists of 0 booking changes or perhaps 1.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = booking_changes))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = booking_changes,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per booking_changes (conditional probability)
df_booking_changes <- bookings_train %>%
group_by(is_cancelled, booking_changes) %>%
summarize(n = n()) %>%
group_by(booking_changes) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_booking_changes, aes(x = booking_changes, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$booking_changes))## [1] 0# Check for bizarre values
sort(unique(bookings_train$booking_changes))## [1] 0 1 2 3 4 5 6 7 8 9 10 13 16# Unique vals + count
sort(table(bookings_train$booking_changes))##
## 10 13 16 9 7 8 6 5 4 3 2 1 0
## 1 1 1 2 3 3 17 45 104 276 938 3280 19364deposit_type
As for the deposit type, we mostly have data on the no deposit type. Furthermore we have non refund and refundable. The non refundable category seems to have a large amount of cancellations proportionally wise.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = deposit_type))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = deposit_type,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per deposit_type (conditional probability)
df_deposit_type <- bookings_train %>%
group_by(is_cancelled, deposit_type) %>%
summarize(n = n()) %>%
group_by(deposit_type) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_deposit_type, aes(x = deposit_type, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$deposit_type))## [1] 0# Check for bizarre values
sort(unique(bookings_train$deposit_type))## [1] No Deposit Non Refund Refundable
## Levels: No Deposit Non Refund Refundable# Unique vals + count
sort(table(bookings_train$deposit_type))##
## Refundable Non Refund No Deposit
## 75 1014 22946days_in_waiting_list
There seems to be no clear pattern in this case, but it can be that the more days waitlisted, the less cancellations there are. However, the sample of the data is not reporesentative for all days. Mostly, the days are 0.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = days_in_waiting_list))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = days_in_waiting_list,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per days_in_waiting_list (conditional probability)
df_days_in_waiting_list <- bookings_train %>%
group_by(is_cancelled, days_in_waiting_list) %>%
summarize(n = n()) %>%
group_by(days_in_waiting_list) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_days_in_waiting_list, aes(x = days_in_waiting_list, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$days_in_waiting_list))## [1] 0# Check for bizarre values
sort(unique(bookings_train$days_in_waiting_list))## [1] 0 1 2 4 5 6 8 11 13 14 18 22 34 43 44 47 50 60 61
## [20] 64 65 75 83 93 97 99 100 101 107 109 113 116 121 122 125 142 150 154
## [39] 185# Unique vals + count
sort(table(bookings_train$days_in_waiting_list))##
## 2 11 13 18 22 34 43 44 83 93 99 109 116
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 142 154 185 5 6 50 60 64 97 100 107 121 4
## 1 1 1 2 2 2 2 2 2 2 2 2 3
## 8 61 1 14 113 150 125 75 101 47 65 122 0
## 3 3 4 4 4 7 8 12 13 16 16 28 23880customer_type
Most customers are apaprently transient, transient-party, contract, group. Transient seems to have the most cancellations and the least cancellations fall under the category of contract.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = customer_type))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = customer_type,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per customer_type (conditional probability)
df_customer_type <- bookings_train %>%
group_by(is_cancelled, customer_type) %>%
summarize(n = n()) %>%
group_by(customer_type) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_customer_type, aes(x = customer_type, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$customer_type))## [1] 0# Check for bizarre values
sort(unique(bookings_train$customer_type))## [1] Transient Transient-Party Contract Group
## Levels: Transient Transient-Party Contract Group# Unique vals + count
sort(table(bookings_train$customer_type))##
## Group Contract Transient-Party Transient
## 179 1043 4662 18151adr
Interestingly, we can observe that the distribution of ADR for cancelled is higher and more disperse than the one of no cancellation. It would indicate that higher values of ADR means higher chance of cancellation.
boxplot(bookings_train$adr~bookings_train$is_cancelled,
xlab="is_cancelled", ylab="ADR",
col=topo.colors(3))
legend("bottomleft", inset=.02, title="Cancellation",
c("Yes","No"), fill=topo.colors(3), horiz=TRUE, cex=0.8)
# Minmax
summary(bookings_train$adr)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -6.38 50.00 75.00 94.58 124.21 450.00table(bookings_train$adr)##
## -6.38 0 1.8 2 2.4 3 4 4.5 5.25 6 7
## 1 468 1 1 1 1 15 2 2 7 2
## 8 8.43 9.14 10 10.5 10.8 11.53 11.75 12 12.5 13
## 62 1 1 2 1 2 1 1 4 1 2
## 14 17.5 19 19.35 20 21 21.37 21.5 22.11 22.4 22.5
## 3 1 2 1 1 3 1 1 1 1 10
## 23 23.7 23.93 24.5 24.7 24.79 24.8 24.95 25 25.11 25.42
## 1 3 2 1 1 1 2 2 94 3 1
## 25.5 25.64 25.65 25.92 26 26.1 26.26 26.5 26.64 26.93 26.99
## 2 1 1 2 46 31 1 2 3 1 1
## 27 27.07 27.35 27.43 27.5 27.53 27.54 27.56 27.6 27.81 27.9
## 43 1 1 1 4 1 1 1 1 1 4
## 28 28.1 28.33 28.5 28.64 28.71 28.79 28.8 29 29.11 29.16
## 5 8 1 4 3 1 1 3 139 4 9
## 29.3 29.6 29.73 29.75 29.78 29.96 29.97 30 30.03 30.24 30.4
## 1 1 1 1 1 1 1 197 1 21 1
## 30.43 30.5 30.6 30.67 30.71 30.8 30.96 30.99 31 31.18 31.2
## 1 6 16 1 1 4 2 1 14 1 3
## 31.41 31.45 31.5 31.59 31.63 31.64 31.65 31.67 31.8 32 32.1
## 1 12 2 1 1 1 1 1 1 51 1
## 32.23 32.24 32.3 32.31 32.34 32.4 32.5 32.6 32.62 32.77 32.8
## 1 2 2 1 1 23 2 2 1 1 2
## 33 33.05 33.08 33.11 33.26 33.27 33.3 33.31 33.6 33.65 33.71
## 11 1 1 2 10 1 41 1 3 1 1
## 33.84 34 34.11 34.2 34.33 34.4 34.43 34.5 34.51 34.56 34.57
## 1 122 1 8 1 28 2 1 1 7 1
## 34.65 34.73 34.79 34.8 34.82 34.85 34.86 35 35.1 35.11 35.12
## 2 2 1 1 1 2 1 252 14 19 3
## 35.15 35.24 35.25 35.33 35.4 35.45 35.46 35.6 35.63 35.64 35.7
## 3 1 1 2 1 1 1 2 1 1 1
## 35.95 35.99 36 36.04 36.05 36.1 36.12 36.13 36.14 36.2 36.24
## 1 1 175 2 21 2 3 4 1 3 2
## 36.25 36.29 36.32 36.4 36.43 36.45 36.5 36.53 36.55 36.61 36.72
## 4 1 1 3 1 8 2 1 2 2 1
## 36.8 36.84 36.86 36.9 36.96 37 37.01 37.05 37.1 37.14 37.2
## 1 1 4 2 15 82 1 1 1 1 1
## 37.29 37.31 37.4 37.43 37.44 37.45 37.5 37.53 37.54 37.56 37.6
## 1 2 19 3 3 1 2 1 3 4 4
## 37.67 37.7 37.71 37.73 37.8 37.83 37.86 37.94 38 38.03 38.04
## 1 4 1 2 144 2 1 1 140 1 1
## 38.07 38.1 38.11 38.12 38.13 38.25 38.32 38.4 38.5 38.55 38.57
## 4 1 2 1 1 2 2 23 7 2 1
## 38.62 38.68 38.7 38.77 38.81 38.83 38.86 38.88 38.92 39 39.04
## 1 1 4 1 3 2 1 1 1 170 1
## 39.06 39.1 39.13 39.15 39.2 39.27 39.3 39.33 39.4 39.41 39.5
## 1 2 2 5 1 1 4 7 7 1 3
## 39.6 39.68 39.74 39.76 39.79 39.8 39.83 39.84 39.93 39.95 39.96
## 10 2 1 1 1 8 2 1 1 2 1
## 39.99 40 40.03 40.05 40.07 40.11 40.14 40.24 40.25 40.29 40.32
## 1 237 1 29 1 1 3 1 1 1 5
## 40.39 40.4 40.5 40.65 40.68 40.7 40.76 40.8 40.81 40.85 40.93
## 1 3 29 1 3 2 2 6 3 2 1
## 40.95 40.96 41 41.04 41.09 41.11 41.18 41.2 41.33 41.37 41.4
## 22 1 12 1 1 3 1 1 3 1 49
## 41.47 41.5 41.51 41.53 41.57 41.58 41.6 41.65 41.75 41.76 41.85
## 1 1 1 2 3 6 9 2 1 4 2
## 41.87 41.89 41.94 41.96 41.97 41.98 42 42.08 42.1 42.11 42.12
## 1 1 1 1 1 9 210 1 1 2 2
## 42.14 42.23 42.24 42.25 42.28 42.3 42.35 42.38 42.4 42.43 42.5
## 1 1 1 1 1 11 2 1 1 9 18
## 42.51 42.56 42.57 42.6 42.65 42.69 42.7 42.75 42.81 42.83 42.9
## 1 1 1 2 2 4 1 2 2 1 1
## 42.95 42.97 43 43.03 43.08 43.11 43.12 43.16 43.19 43.2 43.26
## 12 5 206 1 1 1 2 1 1 41 1
## 43.3 43.33 43.38 43.4 43.41 43.43 43.5 43.65 43.66 43.67 43.7
## 3 2 1 8 4 1 3 1 1 1 1
## 43.71 43.73 43.75 43.76 43.78 43.8 43.88 43.89 43.92 43.93 43.96
## 1 1 1 1 1 5 1 4 1 5 1
## 43.97 44 44.04 44.07 44.1 44.14 44.28 44.3 44.33 44.36 44.38
## 1 92 1 1 8 1 1 1 1 4 1
## 44.4 44.43 44.45 44.46 44.49 44.5 44.56 44.64 44.66 44.67 44.7
## 2 1 1 1 1 57 1 9 7 1 1
## 44.71 44.8 44.81 44.87 44.88 44.99 45 45.05 45.1 45.2 45.33
## 1 23 2 1 2 1 211 1 1 2 1
## 45.35 45.36 45.37 45.45 45.5 45.55 45.6 45.64 45.65 45.66 45.72
## 1 4 1 1 9 1 6 1 1 1 1
## 45.77 45.8 45.89 45.9 46 46.07 46.1 46.16 46.17 46.18 46.2
## 1 1 1 8 160 1 2 1 2 2 2
## 46.33 46.36 46.4 46.43 46.5 46.57 46.64 46.66 46.69 46.7 46.75
## 1 6 37 2 14 1 3 1 1 1 15
## 46.76 46.8 46.83 46.88 46.93 46.96 46.98 47 47.07 47.12 47.14
## 1 30 1 1 1 2 13 98 1 1 1
## 47.2 47.23 47.25 47.33 47.4 47.46 47.5 47.53 47.54 47.55 47.57
## 1 1 2 2 4 1 5 1 3 1 1
## 47.6 47.7 47.72 47.74 47.76 47.79 47.8 47.83 47.85 47.86 47.9
## 3 2 1 1 1 1 6 1 1 1 5
## 47.97 47.98 48 48.06 48.08 48.12 48.18 48.19 48.2 48.25 48.27
## 1 1 621 1 2 1 1 2 2 1 1
## 48.3 48.32 48.38 48.4 48.43 48.5 48.54 48.57 48.6 48.61 48.7
## 1 1 1 4 3 6 1 1 10 1 3
## 48.71 48.75 48.8 48.86 48.88 48.9 48.95 48.96 48.98 49 49.01
## 2 13 9 1 4 2 1 1 1 93 2
## 49.1 49.16 49.17 49.18 49.2 49.22 49.25 49.3 49.4 49.5 49.51
## 1 2 2 1 6 1 1 4 10 12 1
## 49.59 49.6 49.67 49.7 49.71 49.74 49.75 49.8 49.9 49.92 49.94
## 2 3 1 2 1 4 2 2 3 1 1
## 49.95 50 50.02 50.03 50.05 50.06 50.1 50.15 50.17 50.19 50.2
## 26 205 1 2 1 2 2 7 1 1 3
## 50.22 50.24 50.33 50.36 50.4 50.43 50.5 50.54 50.55 50.57 50.59
## 2 1 3 1 31 1 6 1 1 1 1
## 50.6 50.63 50.8 50.83 50.85 50.86 50.88 50.91 50.93 50.95 50.96
## 2 1 2 1 15 1 2 1 2 1 1
## 50.97 50.98 51 51.02 51.04 51.09 51.12 51.13 51.2 51.22 51.25
## 1 1 48 1 2 1 1 2 2 3 2
## 51.27 51.29 51.3 51.36 51.37 51.4 51.43 51.47 51.5 51.59 51.6
## 1 1 4 1 3 2 1 1 4 1 1
## 51.67 51.7 51.71 51.75 51.76 51.8 51.84 51.85 51.87 51.89 51.9
## 1 1 2 3 2 7 8 10 1 1 1
## 51.94 51.95 51.97 52 52.14 52.18 52.2 52.23 52.25 52.27 52.33
## 1 1 1 80 1 1 69 1 2 2 2
## 52.35 52.36 52.4 52.43 52.46 52.5 52.51 52.55 52.56 52.58 52.66
## 1 5 19 1 2 5 1 4 14 1 1
## 52.67 52.7 52.71 52.73 52.75 52.8 52.85 52.86 52.9 52.97 52.98
## 1 3 1 1 1 20 21 2 1 1 2
## 53 53.01 53.06 53.1 53.13 53.14 53.2 53.25 53.33 53.39 53.4
## 37 1 2 9 1 1 5 2 2 1 3
## 53.41 53.44 53.46 53.55 53.57 53.6 53.63 53.67 53.71 53.79 53.8
## 1 1 1 4 2 1 1 3 1 1 1
## 53.82 53.84 53.85 53.86 53.87 53.9 53.99 54 54.02 54.06 54.2
## 1 1 16 1 5 1 1 179 1 1 6
## 54.21 54.4 54.41 54.43 54.47 54.5 54.56 54.6 54.67 54.68 54.7
## 2 10 1 1 1 71 6 1 4 2 2
## 54.71 54.72 54.75 54.8 54.84 54.86 54.9 54.97 54.99 55 55.04
## 5 1 1 2 1 4 8 2 14 264 1
## 55.06 55.08 55.09 55.1 55.11 55.13 55.14 55.2 55.25 55.28 55.3
## 1 1 1 7 1 1 1 2 4 1 1
## 55.32 55.33 55.35 55.39 55.4 55.41 55.43 55.44 55.46 55.48 55.49
## 1 2 1 1 2 1 8 8 1 1 1
## 55.5 55.57 55.59 55.6 55.63 55.64 55.65 55.68 55.69 55.73 55.78
## 4 1 1 2 1 2 1 13 1 1 1
## 55.79 55.8 55.83 55.87 55.89 55.92 56 56.01 56.05 56.1 56.11
## 1 71 2 2 1 1 149 1 1 6 1
## 56.16 56.17 56.2 56.21 56.29 56.33 56.4 56.42 56.45 56.47 56.48
## 3 1 1 3 1 2 8 1 1 1 1
## 56.5 56.52 56.57 56.6 56.63 56.67 56.7 56.71 56.72 56.73 56.83
## 3 2 4 1 1 2 38 1 3 1 1
## 56.88 56.89 56.9 56.95 56.98 57 57.04 57.06 57.09 57.1 57.11
## 1 2 1 4 2 43 1 1 1 1 1
## 57.14 57.15 57.2 57.22 57.23 57.25 57.28 57.29 57.33 57.39 57.4
## 1 1 1 1 1 2 1 1 2 1 3
## 57.43 57.44 57.5 57.51 57.53 57.55 57.6 57.61 57.64 57.65 57.67
## 4 1 7 3 2 2 34 1 2 1 1
## 57.68 57.7 57.72 57.74 57.75 57.76 57.8 57.83 57.85 57.9 57.92
## 1 1 1 1 5 1 23 1 1 2 1
## 57.96 57.97 58 58.04 58.05 58.2 58.22 58.23 58.25 58.32 58.33
## 3 2 233 1 5 10 1 2 1 1 1
## 58.4 58.43 58.5 58.52 58.55 58.57 58.6 58.65 58.7 58.75 58.8
## 9 1 14 2 1 2 8 1 11 1 6
## 58.9 58.91 58.93 58.95 59 59.03 59.04 59.06 59.07 59.08 59.12
## 1 1 1 31 67 1 1 1 2 1 1
## 59.14 59.18 59.2 59.22 59.24 59.28 59.29 59.3 59.31 59.33 59.4
## 1 2 4 2 1 1 2 3 3 4 31
## 59.48 59.5 59.56 59.6 59.62 59.66 59.71 59.73 59.75 59.79 59.8
## 1 3 1 6 1 2 2 1 2 3 13
## 59.85 59.9 59.91 60 60.06 60.07 60.13 60.18 60.2 60.21 60.25
## 2 3 2 279 1 1 2 1 2 1 1
## 60.3 60.35 60.36 60.4 60.43 60.44 60.46 60.48 60.5 60.56 60.61
## 13 5 2 3 1 1 1 1 4 1 1
## 60.64 60.7 60.71 60.74 60.8 60.82 60.83 60.92 60.98 61 61.02
## 1 1 1 1 1 1 2 1 1 50 1
## 61.19 61.2 61.28 61.33 61.35 61.4 61.5 61.56 61.6 61.64 61.65
## 2 31 1 2 2 9 9 3 10 1 4
## 61.67 61.68 61.71 61.75 61.8 61.86 61.87 62 62.04 62.05 62.1
## 1 1 1 1 5 1 1 92 1 2 4
## 62.12 62.16 62.17 62.18 62.2 62.25 62.28 62.29 62.3 62.37 62.38
## 1 1 1 3 4 3 4 3 2 1 1
## 62.4 62.43 62.45 62.48 62.5 62.56 62.57 62.6 62.63 62.64 62.71
## 4 1 1 10 12 1 1 1 1 1 1
## 62.73 62.75 62.8 62.81 62.83 62.88 62.9 62.95 63 63.03 63.07
## 1 2 3 1 2 1 14 1 77 4 1
## 63.13 63.14 63.2 63.21 63.26 63.28 63.29 63.3 63.32 63.36 63.4
## 1 3 3 1 1 1 1 1 1 7 17
## 63.45 63.47 63.5 63.57 63.6 63.61 63.65 63.67 63.68 63.72 63.75
## 1 2 10 1 2 1 4 1 1 2 3
## 63.76 63.79 63.8 63.82 63.86 63.9 63.91 63.95 64 64.08 64.09
## 2 1 1 1 1 8 1 5 157 1 1
## 64.1 64.14 64.18 64.21 64.22 64.25 64.28 64.29 64.33 64.4 64.42
## 5 2 1 1 1 1 1 2 2 2 1
## 64.44 64.49 64.5 64.54 64.6 64.64 64.67 64.75 64.78 64.79 64.8
## 2 1 5 1 3 1 2 1 1 1 41
## 64.83 64.85 64.9 64.95 64.96 64.98 65 65.04 65.07 65.1 65.11
## 3 1 28 1 2 1 231 1 1 2 1
## 65.12 65.19 65.2 65.27 65.3 65.33 65.35 65.37 65.4 65.42 65.43
## 1 1 3 1 1 4 1 1 21 1 2
## 65.44 65.45 65.5 65.52 65.57 65.6 65.61 65.65 65.66 65.7 65.75
## 1 4 9 1 3 6 1 1 1 19 1
## 65.8 65.83 65.84 65.85 65.86 65.87 65.9 66 66.02 66.06 66.15
## 1 3 1 1 1 1 2 240 3 1 6
## 66.2 66.21 66.23 66.24 66.28 66.3 66.34 66.4 66.42 66.43 66.44
## 2 1 2 2 5 3 3 2 4 1 1
## 66.45 66.48 66.5 66.51 66.6 66.63 66.67 66.7 66.71 66.72 66.75
## 1 1 9 1 15 1 2 1 1 1 1
## 66.8 66.84 66.98 67 67.05 67.13 67.14 67.15 67.19 67.2 67.22
## 10 1 2 78 5 1 1 1 1 3 1
## 67.24 67.25 67.31 67.32 67.33 67.37 67.4 67.41 67.44 67.5 67.54
## 2 2 5 1 1 2 2 1 1 42 1
## 67.56 67.57 67.58 67.6 67.67 67.75 67.78 67.79 67.8 67.82 67.88
## 1 1 6 1 1 2 1 2 6 1 1
## 67.92 67.99 68 68.03 68.06 68.08 68.14 68.16 68.17 68.2 68.33
## 1 7 290 1 1 1 1 2 1 3 4
## 68.35 68.4 68.42 68.43 68.45 68.47 68.5 68.53 68.56 68.57 68.6
## 2 45 1 2 2 1 6 3 1 1 2
## 68.61 68.66 68.7 68.72 68.74 68.75 68.8 68.85 68.86 68.88 68.95
## 1 3 1 1 7 4 5 4 3 1 4
## 69 69.02 69.04 69.12 69.13 69.17 69.2 69.21 69.25 69.29 69.3
## 65 2 2 1 1 1 1 1 1 1 3
## 69.33 69.36 69.37 69.4 69.43 69.49 69.5 69.58 69.64 69.66 69.67
## 1 8 1 2 2 2 2 1 1 1 1
## 69.7 69.71 69.83 69.85 69.92 69.95 69.96 69.99 70 70.11 70.15
## 3 3 1 1 1 1 1 1 230 3 1
## 70.17 70.2 70.22 70.25 70.26 70.29 70.3 70.33 70.38 70.4 70.46
## 1 9 1 1 2 1 1 3 1 22 1
## 70.47 70.48 70.5 70.53 70.56 70.6 70.71 70.75 70.8 70.84 70.85
## 1 1 7 1 1 1 1 6 6 3 1
## 70.86 70.88 70.91 70.92 70.95 70.97 70.98 70.99 71 71.04 71.1
## 3 1 1 1 5 1 3 1 46 1 20
## 71.11 71.14 71.2 71.23 71.25 71.28 71.3 71.33 71.39 71.4 71.43
## 1 1 6 2 1 2 1 2 1 1 3
## 71.45 71.5 71.55 71.56 71.57 71.6 71.66 71.69 71.7 71.71 71.73
## 1 1 6 3 1 2 1 5 6 1 1
## 71.76 71.81 71.9 71.93 71.94 71.96 72 72.05 72.06 72.07 72.14
## 1 1 1 1 1 1 228 1 1 1 3
## 72.16 72.18 72.2 72.25 72.31 72.38 72.4 72.41 72.45 72.5 72.55
## 1 1 6 1 1 1 3 1 2 13 1
## 72.6 72.67 72.71 72.72 72.75 72.8 72.83 72.86 72.9 72.91 72.93
## 2 3 1 1 8 3 1 1 12 1 1
## 72.95 73 73.06 73.08 73.1 73.14 73.15 73.18 73.2 73.23 73.25
## 2 72 1 1 3 1 4 1 3 1 1
## 73.28 73.3 73.33 73.35 73.38 73.4 73.41 73.43 73.44 73.46 73.47
## 3 2 23 1 1 3 1 3 1 1 1
## 73.49 73.5 73.6 73.66 73.67 73.68 73.69 73.7 73.71 73.75 73.76
## 1 16 1 1 1 1 1 1 2 2 1
## 73.79 73.8 73.86 73.92 73.95 73.98 74 74.03 74.04 74.06 74.1
## 1 21 1 2 5 1 80 1 2 3 2
## 74.12 74.17 74.2 74.25 74.28 74.33 74.37 74.39 74.4 74.43 74.45
## 1 1 2 29 2 1 1 1 4 1 4
## 74.49 74.5 74.53 74.57 74.6 74.61 74.62 74.67 74.7 74.72 74.75
## 1 3 2 6 4 2 1 1 5 1 5
## 74.76 74.8 74.82 74.9 74.93 75 75.03 75.08 75.09 75.1 75.15
## 2 5 1 2 2 181 1 1 1 2 1
## 75.16 75.2 75.25 75.33 75.34 75.36 75.4 75.43 75.44 75.46 75.5
## 1 3 1 2 1 1 1 2 2 2 1
## 75.52 75.53 75.54 75.57 75.6 75.65 75.75 75.76 75.77 75.8 75.83
## 1 1 1 2 17 2 2 1 1 1 1
## 75.95 75.96 76 76.03 76.05 76.12 76.13 76.19 76.2 76.22 76.23
## 1 1 85 1 7 1 1 1 2 1 2
## 76.24 76.25 76.3 76.38 76.4 76.43 76.5 76.6 76.67 76.75 76.8
## 1 2 1 1 3 2 20 1 13 3 20
## 76.81 76.84 76.86 76.89 76.9 76.93 76.95 77 77.01 77.03 77.1
## 2 1 1 1 1 1 2 35 2 1 2
## 77.14 77.18 77.2 77.22 77.23 77.29 77.3 77.33 77.35 77.36 77.4
## 6 1 1 2 1 1 1 1 2 1 11
## 77.45 77.5 77.57 77.58 77.6 77.61 77.64 77.65 77.7 77.71 77.75
## 1 7 1 1 7 1 2 1 1 1 2
## 77.78 77.79 77.83 77.84 77.85 77.86 77.88 77.9 77.93 77.96 78
## 2 1 1 2 7 1 1 1 1 1 103
## 78.1 78.18 78.19 78.2 78.23 78.25 78.3 78.31 78.33 78.36 78.42
## 1 1 1 4 3 4 7 2 1 1 1
## 78.5 78.55 78.56 78.57 78.6 78.67 78.75 78.8 78.81 78.85 78.87
## 14 1 5 2 3 1 2 2 4 6 1
## 78.93 78.94 78.95 78.98 79 79.05 79.09 79.13 79.14 79.2 79.22
## 1 2 3 1 85 2 1 2 1 25 1
## 79.26 79.3 79.31 79.4 79.41 79.42 79.5 79.54 79.63 79.66 79.67
## 1 1 1 1 1 1 17 1 1 2 1
## 79.74 79.8 79.83 79.84 79.85 79.86 79.88 79.9 79.97 80 80.03
## 2 5 1 2 2 1 1 4 1 270 2
## 80.07 80.09 80.1 80.14 80.16 80.19 80.22 80.23 80.25 80.33 80.4
## 1 1 37 1 1 1 1 1 1 2 5
## 80.43 80.45 80.46 80.47 80.5 80.54 80.57 80.63 80.66 80.71 80.75
## 1 1 1 2 2 1 1 2 1 1 2
## 80.8 80.81 80.85 80.86 80.88 80.94 80.95 80.99 81 81.01 81.02
## 2 8 5 1 1 1 1 1 134 1 1
## 81.05 81.08 81.16 81.18 81.2 81.24 81.29 81.31 81.36 81.4 81.43
## 1 1 1 1 9 1 1 1 1 1 2
## 81.45 81.5 81.51 81.53 81.57 81.6 81.62 81.67 81.69 81.71 81.75
## 2 7 2 1 1 5 2 3 1 2 1
## 81.76 81.79 81.8 81.82 81.85 81.9 81.98 82 82.02 82.03 82.07
## 4 1 2 2 4 5 1 84 1 1 1
## 82.1 82.17 82.2 82.23 82.25 82.29 82.31 82.33 82.35 82.4 82.44
## 4 2 1 1 1 2 2 1 4 1 1
## 82.49 82.5 82.57 82.63 82.67 82.68 82.69 82.71 82.73 82.74 82.76
## 1 6 1 1 2 1 2 2 1 1 1
## 82.8 82.83 82.85 82.86 82.88 82.95 83 83.05 83.15 83.16 83.2
## 17 1 3 5 5 1 45 2 1 2 2
## 83.23 83.25 83.26 83.3 83.33 83.4 83.43 83.47 83.5 83.6 83.66
## 1 2 1 3 2 1 7 1 6 4 1
## 83.67 83.7 83.71 83.76 83.79 83.8 83.81 83.84 83.85 83.86 83.9
## 2 8 1 3 1 3 1 1 1 1 2
## 83.93 83.94 84 84.01 84.12 84.15 84.21 84.23 84.24 84.28 84.29
## 1 1 113 1 2 3 2 1 1 1 1
## 84.39 84.4 84.47 84.49 84.5 84.58 84.6 84.7 84.76 84.8 84.83
## 1 1 1 2 4 1 10 6 1 6 1
## 84.95 84.96 85 85.05 85.08 85.1 85.14 85.2 85.3 85.33 85.4
## 2 2 278 2 1 1 2 5 5 29 1
## 85.5 85.59 85.6 85.67 85.7 85.72 85.73 85.75 85.8 85.84 85.86
## 23 1 1 1 6 1 1 2 3 1 1
## 85.88 86 86.1 86.13 86.14 86.16 86.17 86.2 86.22 86.23 86.25
## 2 77 2 1 2 2 1 4 1 1 1
## 86.28 86.29 86.33 86.4 86.43 86.46 86.5 86.55 86.57 86.6 86.7
## 2 3 2 9 1 2 2 14 1 1 8
## 86.71 86.79 86.83 86.86 86.91 86.95 87 87.05 87.07 87.12 87.19
## 1 1 1 1 4 3 64 4 1 1 1
## 87.2 87.3 87.4 87.43 87.45 87.46 87.48 87.5 87.53 87.55 87.59
## 1 4 1 1 1 2 2 6 1 1 1
## 87.6 87.64 87.65 87.67 87.68 87.75 87.78 87.8 87.86 87.9 87.91
## 1 1 1 1 1 1 1 1 1 3 2
## 88 88.14 88.2 88.22 88.29 88.3 88.33 88.4 88.5 88.53 88.55
## 56 1 27 1 1 1 1 16 5 1 2
## 88.6 88.61 88.67 88.68 88.7 88.71 88.73 88.74 88.75 88.8 88.84
## 1 1 4 1 1 1 1 2 1 3 2
## 88.85 88.86 88.9 88.95 89 89.06 89.08 89.1 89.14 89.17 89.18
## 1 1 2 1 83 2 1 8 2 1 1
## 89.2 89.22 89.25 89.3 89.34 89.4 89.43 89.49 89.5 89.55 89.59
## 15 1 3 1 1 2 1 1 2 3 1
## 89.6 89.67 89.68 89.7 89.75 89.77 89.78 89.84 89.91 89.98 90
## 1 2 1 2 1 1 1 1 2 1 146
## 90.03 90.06 90.1 90.12 90.14 90.17 90.2 90.25 90.38 90.4 90.43
## 1 1 14 1 1 1 1 1 1 1 2
## 90.49 90.5 90.6 90.61 90.64 90.67 90.68 90.71 90.8 90.86 90.9
## 2 2 2 1 2 3 1 2 2 1 6
## 90.95 90.96 91 91.06 91.1 91.18 91.2 91.25 91.3 91.35 91.36
## 19 1 32 1 1 1 8 1 1 1 2
## 91.37 91.44 91.46 91.5 91.55 91.58 91.67 91.75 91.76 91.77 91.8
## 1 1 1 9 1 1 6 1 1 1 3
## 91.81 91.85 91.86 91.9 91.92 91.93 91.95 91.96 92 92.09 92.1
## 1 2 1 2 1 1 1 1 103 1 8
## 92.14 92.17 92.19 92.25 92.29 92.33 92.34 92.4 92.42 92.43 92.45
## 2 1 1 2 1 1 1 3 1 1 1
## 92.5 92.55 92.57 92.58 92.6 92.63 92.67 92.7 92.71 92.8 92.82
## 3 2 4 1 1 3 2 1 1 2 1
## 92.85 92.86 92.89 92.9 92.95 93 93.06 93.12 93.17 93.2 93.21
## 1 1 2 3 1 54 1 1 1 2 1
## 93.29 93.3 93.38 93.5 93.54 93.57 93.6 93.62 93.67 93.75 93.77
## 2 1 5 5 1 3 13 1 1 4 1
## 93.8 93.81 93.86 93.88 93.93 93.94 94 94.14 94.16 94.2 94.25
## 5 1 3 1 1 1 35 1 2 1 1
## 94.26 94.29 94.37 94.4 94.41 94.48 94.5 94.6 94.62 94.63 94.71
## 1 4 4 1 1 1 15 1 1 2 1
## 94.73 94.75 94.8 94.86 94.89 94.95 95 95.02 95.04 95.07 95.2
## 1 1 1 1 1 1 93 1 1 1 2
## 95.25 95.27 95.33 95.4 95.5 95.57 95.6 95.64 95.67 95.71 95.85
## 1 1 1 5 6 2 4 1 4 1 1
## 95.91 95.95 96 96.13 96.14 96.2 96.21 96.25 96.26 96.3 96.33
## 1 1 43 2 1 1 1 2 1 22 1
## 96.36 96.4 96.43 96.49 96.5 96.56 96.67 96.8 96.83 96.9 96.94
## 1 4 1 1 2 1 3 2 1 2 2
## 96.95 97 97.02 97.1 97.2 97.21 97.25 97.29 97.3 97.33 97.4
## 1 31 1 1 7 1 3 2 7 1 5
## 97.41 97.46 97.5 97.6 97.67 97.7 97.71 97.74 97.75 97.8 97.88
## 1 1 11 1 1 2 4 1 4 1 1
## 97.99 98 98.02 98.04 98.05 98.1 98.14 98.25 98.33 98.4 98.5
## 2 64 2 2 1 3 1 1 3 12 1
## 98.6 98.63 98.64 98.67 98.72 98.74 98.75 98.76 98.88 99 99.02
## 5 1 1 2 1 1 3 1 1 78 1
## 99.06 99.15 99.18 99.21 99.22 99.24 99.28 99.3 99.32 99.33 99.43
## 1 1 1 1 1 3 1 1 1 1 2
## 99.45 99.5 99.51 99.63 99.64 99.7 99.8 99.81 99.85 99.9 100
## 1 9 1 2 1 3 2 1 1 4 84
## 100.1 100.2 100.25 100.29 100.3 100.33 100.4 100.44 100.5 100.55 100.67
## 1 7 2 1 1 4 3 1 2 2 2
## 100.7 100.71 100.8 100.85 100.86 100.87 100.89 100.91 101 101.16 101.2
## 1 2 15 1 3 1 1 1 35 1 1
## 101.21 101.22 101.25 101.27 101.29 101.33 101.4 101.43 101.5 101.53 101.65
## 1 1 3 1 1 2 1 3 4 1 5
## 101.67 101.68 101.7 101.71 101.75 101.8 101.87 102 102.2 102.21 102.25
## 2 1 12 2 1 1 1 23 1 1 2
## 102.3 102.31 102.32 102.33 102.35 102.38 102.41 102.43 102.45 102.49 102.5
## 1 1 1 2 1 1 2 1 1 1 7
## 102.51 102.55 102.6 102.65 102.66 102.71 102.73 102.8 102.86 102.9 102.93
## 1 1 5 1 1 1 1 13 1 1 1
## 103 103.05 103.07 103.11 103.18 103.2 103.22 103.28 103.3 103.33 103.35
## 16 3 1 1 4 4 1 1 1 1 1
## 103.36 103.37 103.5 103.51 103.59 103.6 103.67 103.68 103.7 103.75 103.76
## 1 1 8 1 1 1 1 1 2 4 1
## 103.8 103.95 104 104.1 104.16 104.17 104.25 104.33 104.49 104.5 104.55
## 1 1 27 1 1 1 1 2 1 4 8
## 104.6 104.67 104.68 104.71 104.72 104.73 104.75 104.8 104.81 104.85 104.91
## 2 3 1 1 3 1 1 5 1 17 1
## 104.94 104.97 104.98 105 105.11 105.13 105.2 105.22 105.25 105.3 105.31
## 1 1 1 75 1 1 3 1 1 1 1
## 105.34 105.4 105.47 105.5 105.61 105.66 105.67 105.69 105.71 105.77 105.8
## 1 1 2 6 1 1 1 1 2 1 1
## 105.81 106 106.06 106.09 106.13 106.15 106.2 106.21 106.25 106.26 106.3
## 1 37 1 1 1 1 4 1 1 1 1
## 106.31 106.33 106.35 106.36 106.4 106.47 106.5 106.55 106.56 106.57 106.64
## 1 1 4 1 1 1 11 6 3 2 1
## 106.67 106.7 106.71 106.8 106.84 106.85 106.86 106.9 107 107.05 107.1
## 1 1 1 2 1 11 2 1 60 1 17
## 107.17 107.2 107.29 107.3 107.33 107.38 107.4 107.42 107.5 107.57 107.6
## 1 8 1 1 1 1 1 1 5 1 3
## 107.63 107.67 107.71 107.75 107.8 107.82 107.83 108 108.02 108.06 108.09
## 1 2 1 1 1 1 1 45 1 1 1
## 108.1 108.15 108.17 108.24 108.26 108.3 108.31 108.33 108.34 108.36 108.4
## 1 1 2 2 2 3 1 3 1 1 2
## 108.5 108.54 108.56 108.57 108.65 108.7 108.71 108.73 108.75 108.8 108.83
## 1 1 3 1 2 1 1 1 1 2 1
## 108.86 108.88 108.9 108.91 108.92 108.97 108.99 109 109.03 109.1 109.13
## 1 1 7 1 1 1 1 32 2 3 1
## 109.2 109.3 109.33 109.36 109.5 109.63 109.65 109.71 109.75 109.8 109.83
## 2 1 1 1 6 1 2 1 1 7 1
## 109.86 109.9 109.96 110 110.05 110.2 110.25 110.3 110.32 110.33 110.36
## 1 1 1 91 1 2 1 2 1 2 1
## 110.4 110.43 110.5 110.6 110.64 110.67 110.7 110.71 110.72 110.75 110.8
## 1 1 9 1 1 1 39 1 1 3 2
## 110.83 110.94 111 111.14 111.15 111.2 111.25 111.27 111.3 111.5 111.6
## 1 1 22 2 4 2 2 1 1 4 2
## 111.67 111.73 111.75 111.78 111.8 111.94 111.96 112 112.05 112.12 112.16
## 1 1 4 2 2 1 1 34 2 1 1
## 112.2 112.22 112.33 112.36 112.4 112.41 112.42 112.5 112.57 112.6 112.63
## 4 3 1 2 1 1 2 14 1 2 4
## 112.7 112.71 112.8 112.88 112.95 113 113.05 113.1 113.14 113.19 113.2
## 4 1 4 1 1 29 7 1 1 1 1
## 113.3 113.31 113.33 113.4 113.52 113.55 113.57 113.6 113.67 113.7 113.75
## 1 1 1 7 1 1 2 2 1 1 1
## 113.8 113.9 113.91 114 114.04 114.14 114.15 114.2 114.29 114.3 114.33
## 1 3 1 41 1 2 1 3 2 1 1
## 114.48 114.5 114.51 114.6 114.65 114.71 114.73 114.75 114.76 114.78 114.8
## 1 5 1 1 1 1 1 4 1 1 2
## 114.9 114.95 115 115.2 115.3 115.33 115.4 115.5 115.55 115.67 115.68
## 3 1 88 20 1 1 2 8 1 2 1
## 115.7 115.74 115.75 115.76 115.8 115.82 115.85 115.92 115.93 116 116.1
## 1 2 2 1 1 1 1 1 1 22 17
## 116.18 116.33 116.4 116.43 116.5 116.53 116.57 116.6 116.61 116.64 116.67
## 2 1 2 3 14 1 1 3 1 2 1
## 116.7 116.71 116.75 116.8 116.85 116.9 117 117.04 117.19 117.2 117.21
## 1 2 1 1 2 2 38 2 1 6 1
## 117.22 117.25 117.29 117.3 117.43 117.48 117.5 117.6 117.63 117.73 117.74
## 2 1 2 4 1 1 1 2 1 1 1
## 117.75 117.76 117.81 117.83 117.84 117.9 118 118.02 118.06 118.1 118.13
## 1 1 3 1 1 8 31 1 1 3 1
## 118.17 118.19 118.2 118.24 118.25 118.29 118.3 118.35 118.4 118.5 118.57
## 1 1 2 1 1 1 1 1 2 9 1
## 118.62 118.67 118.7 118.71 118.8 118.84 118.86 118.99 119 119.04 119.08
## 1 2 2 1 5 1 1 1 33 1 2
## 119.1 119.16 119.2 119.25 119.29 119.33 119.35 119.43 119.5 119.64 119.7
## 2 1 2 7 1 1 2 1 1 1 13
## 119.71 119.75 119.76 119.85 119.9 119.97 120 120.02 120.1 120.14 120.16
## 1 1 1 2 2 1 96 1 2 1 1
## 120.2 120.27 120.3 120.33 120.38 120.4 120.43 120.5 120.57 120.6 120.71
## 1 1 1 1 5 1 3 1 1 34 1
## 120.8 120.9 121 121.01 121.17 121.25 121.28 121.33 121.35 121.4 121.47
## 1 1 26 1 1 3 1 4 1 2 1
## 121.5 121.6 121.67 121.7 121.8 121.85 121.93 121.98 122 122.23 122.29
## 32 1 1 1 1 1 1 1 27 1 1
## 122.33 122.4 122.43 122.5 122.53 122.57 122.67 122.7 122.75 122.85 122.89
## 5 2 2 4 1 1 2 2 2 2 1
## 123 123.1 123.12 123.2 123.21 123.25 123.28 123.3 123.33 123.38 123.4
## 64 1 1 5 1 1 1 2 4 2 1
## 123.43 123.5 123.57 123.6 123.62 123.64 123.67 123.68 123.71 123.74 123.77
## 1 14 1 2 1 2 1 1 1 1 1
## 123.88 123.97 124 124.1 124.14 124.2 124.22 124.33 124.38 124.4 124.45
## 1 2 20 1 5 3 1 1 1 1 1
## 124.5 124.55 124.58 124.7 124.74 124.8 124.82 125 125.1 125.2 125.22
## 4 1 1 1 1 2 1 40 2 1 1
## 125.25 125.3 125.4 125.43 125.46 125.5 125.51 125.52 125.54 125.58 125.6
## 1 2 1 3 1 3 1 1 1 1 1
## 125.71 125.82 125.89 125.9 126 126.1 126.15 126.2 126.3 126.33 126.4
## 1 1 1 3 55 1 1 2 3 2 2
## 126.41 126.5 126.57 126.6 126.64 126.65 126.67 126.71 126.74 126.8 126.86
## 1 2 1 1 2 2 4 2 1 5 1
## 126.9 127 127.03 127.14 127.2 127.25 127.26 127.27 127.3 127.31 127.5
## 2 33 1 2 2 2 1 1 1 1 5
## 127.58 127.59 127.67 127.71 127.75 127.8 127.82 127.83 127.9 128 128.04
## 1 1 3 3 2 2 1 1 1 36 1
## 128.05 128.07 128.14 128.2 128.25 128.28 128.35 128.44 128.57 128.65 128.7
## 1 1 1 1 1 1 1 1 1 1 4
## 128.8 128.9 129 129.16 129.2 129.27 129.29 129.33 129.4 129.48 129.5
## 2 3 57 1 3 1 2 1 1 1 2
## 129.57 129.58 129.6 129.7 129.72 129.77 129.86 129.9 129.92 130 130.01
## 1 1 3 1 3 1 1 1 1 32 2
## 130.05 130.06 130.1 130.13 130.25 130.33 130.34 130.35 130.4 130.43 130.5
## 4 1 1 2 1 1 1 1 1 1 9
## 130.55 130.67 130.75 130.8 130.82 130.87 130.9 131 131.06 131.16 131.2
## 4 3 1 2 1 1 5 37 1 1 1
## 131.25 131.29 131.4 131.43 131.5 131.59 131.6 131.63 131.67 131.85 131.86
## 1 1 6 1 4 1 6 2 2 2 1
## 132 132.05 132.16 132.19 132.29 132.3 132.33 132.36 132.44 132.5 132.56
## 17 2 1 1 1 3 1 1 3 6 1
## 132.6 132.67 132.8 132.85 132.86 132.87 132.9 132.91 132.96 133 133.07
## 5 1 2 4 2 1 1 1 1 36 1
## 133.14 133.17 133.2 133.21 133.25 133.29 133.4 133.5 133.67 133.74 133.75
## 1 1 5 1 1 1 2 3 1 2 2
## 134 134.06 134.1 134.14 134.3 134.4 134.43 134.5 134.67 134.73 134.75
## 84 1 8 1 1 1 2 2 2 3 1
## 134.85 134.86 135 135.1 135.13 135.14 135.15 135.2 135.25 135.28 135.29
## 8 1 98 2 1 1 1 3 1 1 1
## 135.3 135.31 135.33 135.4 135.49 135.5 135.57 135.6 135.63 135.67 135.9
## 1 1 1 1 1 3 1 3 1 1 9
## 136 136.08 136.14 136.2 136.44 136.5 136.6 136.67 136.71 136.73 136.8
## 32 1 1 2 1 17 1 1 2 2 1
## 136.85 137 137.14 137.2 137.21 137.33 137.43 137.5 137.57 137.65 137.67
## 1 21 1 1 1 1 2 4 1 1 3
## 137.7 137.71 137.75 137.8 137.9 138 138.01 138.09 138.13 138.25 138.29
## 5 2 2 3 1 12 1 1 1 1 1
## 138.38 138.4 138.5 138.6 138.64 138.67 138.79 138.8 138.86 139 139.02
## 17 1 4 6 1 1 1 3 1 25 1
## 139.05 139.1 139.18 139.2 139.33 139.35 139.4 139.48 139.5 139.52 139.54
## 2 1 1 2 1 1 2 1 5 1 1
## 139.67 139.68 139.7 139.72 139.75 139.9 139.95 139.99 140 140.16 140.21
## 1 3 1 1 1 1 1 1 78 1 1
## 140.25 140.31 140.33 140.38 140.4 140.43 140.57 140.6 140.63 140.7 140.71
## 1 1 3 2 4 1 1 2 1 1 1
## 140.8 140.9 140.94 140.98 141 141.05 141.3 141.33 141.4 141.5 141.55
## 3 1 1 1 25 2 1 3 1 2 2
## 141.57 141.6 141.67 141.68 141.7 141.71 141.91 142 142.03 142.1 142.2
## 1 3 3 1 1 1 1 26 1 1 3
## 142.21 142.22 142.3 142.36 142.48 142.5 142.53 142.63 142.64 142.65 142.67
## 1 1 1 1 1 7 1 1 1 1 2
## 142.68 142.71 142.86 142.97 143 143.1 143.2 143.25 143.28 143.29 143.33
## 1 1 1 1 13 7 2 1 1 1 2
## 143.34 143.4 143.43 143.5 143.55 143.57 143.6 143.73 143.86 143.97 144
## 2 1 2 3 1 2 1 1 1 1 26
## 144.14 144.4 144.43 144.5 144.57 144.65 144.75 144.76 144.8 144.88 144.9
## 1 3 2 3 1 1 2 1 1 1 3
## 145 145.02 145.1 145.14 145.2 145.23 145.33 145.4 145.44 145.5 145.53
## 43 1 2 1 2 1 1 2 1 2 2
## 145.6 145.67 145.71 145.75 145.8 145.87 145.88 146 146.1 146.11 146.22
## 2 3 2 2 1 3 1 30 2 1 1
## 146.25 146.3 146.33 146.38 146.4 146.46 146.5 146.67 146.7 146.71 146.75
## 2 4 3 1 2 1 3 4 2 1 3
## 146.93 146.94 146.95 147 147.02 147.07 147.13 147.2 147.36 147.43 147.6
## 1 1 2 35 1 1 1 1 1 1 5
## 147.67 147.69 147.7 147.71 147.74 147.84 147.86 147.9 147.94 148 148.07
## 2 1 1 1 1 1 1 3 1 6 1
## 148.1 148.12 148.14 148.17 148.25 148.29 148.3 148.34 148.4 148.5 148.57
## 3 1 1 1 2 1 1 1 1 5 1
## 148.6 148.7 148.75 148.79 149 149.14 149.18 149.2 149.29 149.33 149.38
## 1 1 1 1 24 2 1 3 1 1 1
## 149.4 149.45 149.5 149.52 149.63 149.71 149.74 149.81 150 150.22 150.25
## 4 3 4 1 1 1 1 1 40 1 2
## 150.43 150.48 150.5 150.53 150.57 150.58 150.6 150.69 150.71 150.8 150.81
## 1 2 1 2 1 1 3 1 3 1 1
## 150.82 150.83 150.92 151 151.1 151.14 151.19 151.2 151.23 151.25 151.29
## 1 1 1 23 1 4 1 1 1 1 1
## 151.3 151.33 151.4 151.43 151.5 151.56 151.64 151.67 151.76 151.87 152
## 2 2 1 1 6 1 1 1 2 1 21
## 152.1 152.15 152.16 152.2 152.25 152.28 152.3 152.33 152.38 152.43 152.5
## 6 1 1 1 1 1 1 1 3 1 4
## 152.71 152.8 152.86 152.9 153 153.03 153.07 153.1 153.23 153.25 153.29
## 1 1 1 1 44 1 1 2 1 2 1
## 153.37 153.39 153.42 153.5 153.57 153.6 153.68 153.71 153.75 153.9 153.96
## 1 1 2 5 3 1 2 1 20 1 1
## 154 154.08 154.1 154.25 154.29 154.3 154.32 154.35 154.38 154.4 154.5
## 56 2 1 1 1 2 1 1 2 1 4
## 154.54 154.6 154.71 154.77 154.8 154.98 155 155.11 155.25 155.33 155.5
## 1 1 1 1 8 3 34 1 1 1 3
## 155.52 155.54 155.57 155.6 155.64 155.67 155.7 155.71 155.8 155.83 155.86
## 3 1 2 1 1 1 3 1 2 1 1
## 156 156.1 156.22 156.34 156.4 156.45 156.48 156.5 156.51 156.54 156.6
## 16 1 1 1 2 1 1 1 1 1 6
## 156.67 156.73 156.83 156.96 157 157.08 157.2 157.25 157.27 157.28 157.31
## 1 1 1 1 13 3 4 2 1 2 1
## 157.37 157.42 157.5 157.52 157.53 157.59 157.6 157.67 157.7 157.8 157.82
## 1 1 5 1 2 1 1 4 1 1 1
## 157.85 157.86 157.87 157.99 158 158.1 158.16 158.2 158.3 158.33 158.35
## 1 1 1 1 20 2 1 1 1 2 1
## 158.38 158.4 158.5 158.57 158.58 158.6 158.63 158.67 158.73 158.75 158.77
## 1 1 3 1 1 1 1 2 1 1 1
## 158.85 158.86 158.9 159 159.07 159.14 159.2 159.28 159.29 159.33 159.34
## 3 1 2 28 2 2 2 1 1 2 1
## 159.35 159.38 159.5 159.6 159.74 159.75 159.85 160 160.08 160.17 160.2
## 1 1 8 1 1 1 1 80 1 1 2
## 160.33 160.43 160.48 160.5 160.53 160.67 160.68 160.7 160.71 160.75 160.98
## 1 2 1 2 1 1 1 1 3 1 2
## 161 161.08 161.1 161.25 161.29 161.33 161.4 161.45 161.5 161.6 161.67
## 24 1 3 1 3 1 4 1 4 2 2
## 161.71 161.75 161.8 161.86 162 162.1 162.12 162.14 162.15 162.24 162.29
## 1 1 2 2 24 1 1 3 1 1 1
## 162.3 162.47 162.5 162.56 162.71 162.86 162.94 163 163.05 163.08 163.2
## 2 1 2 1 1 1 1 13 1 1 1
## 163.25 163.33 163.35 163.43 163.48 163.5 163.6 163.65 163.67 163.71 163.8
## 1 4 1 1 1 3 1 1 1 1 4
## 164 164.02 164.1 164.13 164.15 164.29 164.4 164.57 164.6 164.63 164.65
## 46 1 1 1 2 2 2 1 1 1 1
## 164.67 164.71 164.8 165 165.08 165.1 165.11 165.15 165.23 165.25 165.33
## 1 1 2 37 1 2 1 1 2 1 2
## 165.37 165.38 165.4 165.5 165.55 165.6 165.63 165.71 165.75 165.8 166
## 1 1 1 2 2 4 1 1 4 2 30
## 166.03 166.05 166.14 166.24 166.43 166.45 166.5 166.56 166.6 166.67 166.71
## 2 2 2 1 1 1 5 1 3 2 1
## 166.75 166.91 166.92 167 167.09 167.17 167.18 167.2 167.23 167.25 167.28
## 1 1 1 20 1 1 7 5 1 3 1
## 167.4 167.43 167.5 167.52 167.54 167.6 167.73 167.75 167.8 167.85 168
## 1 1 2 1 1 1 1 1 1 2 16
## 168.13 168.14 168.15 168.25 168.3 168.33 168.38 168.5 168.57 168.6 168.63
## 1 1 1 1 1 1 1 1 1 3 4
## 168.67 168.7 168.71 168.72 168.75 168.8 168.89 168.93 169 169.08 169.2
## 1 1 1 1 1 3 1 1 34 1 1
## 169.36 169.37 169.43 169.48 169.5 169.6 169.67 169.77 169.83 169.98 170
## 1 1 1 4 5 2 1 1 1 1 31
## 170.1 170.2 170.24 170.33 170.57 170.6 170.67 170.7 170.93 170.95 171
## 1 1 1 2 1 1 1 2 1 1 30
## 171.14 171.18 171.2 171.25 171.33 171.39 171.5 171.53 171.71 171.9 171.94
## 1 1 3 1 1 1 5 1 2 8 2
## 172 172.1 172.38 172.4 172.5 172.6 172.63 172.67 172.71 172.8 172.81
## 53 1 1 1 1 1 1 3 2 9 1
## 172.86 172.9 173 173.05 173.1 173.2 173.21 173.25 173.45 173.5 173.6
## 1 1 26 1 2 1 1 2 1 2 1
## 173.67 173.75 173.79 174 174.01 174.04 174.14 174.25 174.4 174.54 174.55
## 2 1 1 24 1 1 1 1 1 1 1
## 174.6 174.7 174.71 174.76 174.8 174.92 174.95 175 175.14 175.17 175.2
## 2 1 1 1 3 1 1 25 1 1 2
## 175.23 175.24 175.26 175.33 175.34 175.4 175.5 175.56 175.57 175.58 175.71
## 2 1 1 1 1 1 7 1 1 1 1
## 175.8 176 176.16 176.18 176.25 176.4 176.43 176.44 176.64 176.67 176.78
## 1 20 1 1 2 1 2 1 1 2 1
## 176.8 176.86 176.9 177 177.14 177.17 177.25 177.41 177.47 177.5 177.57
## 2 1 1 9 2 1 1 1 1 7 2
## 177.6 177.62 177.67 177.79 177.8 178 178.1 178.13 178.2 178.29 178.31
## 1 1 1 1 1 15 1 1 1 1 1
## 178.33 178.39 178.43 178.46 178.5 178.56 178.64 178.67 178.75 178.88 179
## 2 1 1 1 1 1 1 2 3 1 41
## 179.06 179.1 179.2 179.34 179.4 179.41 179.6 179.63 179.66 179.71 179.77
## 1 5 2 1 2 1 1 1 1 1 1
## 179.81 179.83 179.86 179.88 179.94 180 180.02 180.18 180.33 180.34 180.38
## 1 1 1 1 1 55 1 1 1 1 1
## 180.42 180.47 180.5 180.55 180.67 180.75 180.8 181 181.11 181.14 181.22
## 1 1 1 1 1 1 3 20 1 1 1
## 181.3 181.31 181.33 181.43 181.5 181.54 181.6 181.8 181.88 181.9 181.94
## 1 1 1 1 2 1 1 4 1 2 1
## 182 182.06 182.1 182.16 182.2 182.25 182.33 182.4 182.5 182.6 182.8
## 17 1 1 1 3 3 3 1 2 1 1
## 182.86 182.92 183 183.05 183.16 183.33 183.45 183.5 183.6 183.65 183.75
## 3 1 11 1 1 3 1 2 3 1 9
## 183.83 183.86 183.99 184 184.25 184.29 184.33 184.45 184.5 184.54 184.55
## 1 2 1 23 1 1 1 3 12 1 1
## 184.6 184.64 184.73 184.8 184.86 185 185.1 185.25 185.33 185.37 185.4
## 1 1 1 1 1 18 1 1 1 1 4
## 185.43 185.44 185.46 185.5 185.57 185.6 185.71 185.75 185.96 185.99 186
## 1 1 1 1 1 1 2 1 1 2 11
## 186.02 186.13 186.25 186.29 186.33 186.5 186.6 186.67 186.68 186.74 186.83
## 1 1 2 1 1 3 2 1 1 1 1
## 186.88 186.9 187 187.14 187.15 187.19 187.2 187.24 187.25 187.33 187.5
## 2 1 9 2 1 1 1 1 2 2 6
## 187.52 187.53 187.57 187.64 187.7 187.71 187.8 187.88 188 188.03 188.1
## 1 1 1 1 1 1 1 1 9 1 1
## 188.14 188.29 188.38 188.4 188.43 188.6 188.65 188.67 188.71 188.75 188.78
## 2 1 1 2 1 2 2 1 1 1 1
## 188.86 188.89 189 189.1 189.19 189.2 189.25 189.33 189.42 189.43 189.5
## 1 1 47 1 1 1 1 3 3 1 1
## 189.6 189.67 189.75 189.86 190 190.1 190.18 190.2 190.23 190.27 190.33
## 2 2 2 1 26 1 1 1 2 1 1
## 190.35 190.4 190.6 190.67 190.7 190.88 190.96 190.97 191 191.14 191.41
## 1 1 1 1 2 2 2 1 29 1 1
## 191.43 191.5 191.55 191.6 191.67 191.7 192 192.2 192.33 192.38 192.5
## 1 1 1 3 1 1 26 1 1 1 7
## 192.54 192.6 192.66 192.67 192.68 192.8 193 193.03 193.1 193.12 193.13
## 1 3 2 3 1 2 14 1 2 1 2
## 193.16 193.29 193.33 193.43 193.5 193.6 193.67 193.71 193.77 193.81 193.83
## 1 1 2 1 2 1 2 1 1 1 1
## 193.87 194 194.29 194.33 194.49 194.5 194.71 194.73 194.86 194.9 195
## 1 47 1 1 1 2 1 1 1 1 31
## 195.26 195.33 195.4 195.43 195.45 195.5 195.58 195.6 195.67 195.8 195.86
## 1 1 1 2 2 1 1 3 1 2 1
## 195.95 195.98 196 196.25 196.33 196.34 196.42 196.5 196.52 196.67 196.71
## 1 1 24 2 1 1 1 2 1 1 1
## 196.92 196.93 197 197.1 197.14 197.4 197.6 197.61 197.67 197.7 197.8
## 1 1 6 3 1 1 2 1 2 3 1
## 197.83 197.9 198 198.23 198.29 198.31 198.33 198.4 198.5 198.8 199
## 1 1 19 1 1 1 4 1 3 1 38
## 199.1 199.15 199.2 199.26 199.29 199.4 199.5 199.6 199.63 199.67 199.75
## 1 1 2 1 2 3 2 1 1 1 1
## 200 200.05 200.1 200.43 200.57 200.67 200.7 200.71 200.8 200.86 201
## 42 1 1 1 1 2 2 1 1 1 18
## 201.05 201.14 201.25 201.33 201.4 201.43 201.5 201.6 201.67 201.8 201.86
## 1 1 1 1 1 2 4 6 1 1 2
## 202 202.13 202.33 202.5 202.51 202.7 202.77 202.83 202.88 203 203.1
## 21 1 1 6 1 1 1 1 1 13 1
## 203.13 203.18 203.19 203.21 203.4 203.6 203.67 203.71 203.75 203.8 204
## 1 1 1 1 1 1 3 2 2 1 13
## 204.1 204.16 204.27 204.29 204.33 204.5 204.62 204.67 204.7 204.74 204.75
## 1 2 1 1 2 1 1 1 1 1 2
## 204.8 204.93 205 205.1 205.14 205.2 205.25 205.33 205.5 205.6 205.67
## 1 1 24 1 1 1 1 1 3 1 3
## 205.71 206 206.03 206.1 206.12 206.2 206.22 206.33 206.36 206.4 206.5
## 1 21 2 2 1 1 1 2 1 1 1
## 206.51 206.78 206.86 207 207.05 207.1 207.18 207.2 207.23 207.25 207.29
## 1 1 1 18 1 1 1 1 1 1 1
## 207.3 207.33 207.4 207.5 207.52 207.67 207.76 207.8 208 208.02 208.03
## 1 1 1 4 1 1 1 1 7 1 1
## 208.05 208.08 208.14 208.28 208.33 208.34 208.43 208.5 208.57 208.75 208.8
## 1 1 1 1 1 1 1 3 1 1 2
## 208.86 209 209.1 209.2 209.29 209.33 209.6 209.71 209.75 209.8 209.86
## 1 46 4 1 1 1 2 2 1 1 2
## 210 210.14 210.43 210.6 210.75 210.78 210.86 211 211.12 211.2 211.25
## 32 1 1 1 1 1 2 38 1 2 1
## 211.5 211.6 211.67 211.8 212 212.14 212.16 212.29 212.31 212.5 212.54
## 2 1 1 1 23 3 1 1 1 5 1
## 212.58 212.6 212.71 212.73 212.8 212.88 213 213.33 213.4 213.43 213.5
## 1 1 1 1 2 1 11 2 1 1 2
## 213.57 213.6 213.67 213.7 213.71 213.75 214 214.33 214.6 214.67 214.71
## 1 1 1 1 1 5 23 1 1 1 1
## 214.84 214.86 214.87 214.92 214.99 215 215.24 215.33 215.6 215.67 215.71
## 2 1 1 1 1 9 1 3 2 1 1
## 216 216.13 216.25 216.33 216.43 216.5 216.66 216.67 216.84 216.89 216.93
## 19 1 1 1 1 1 1 2 1 1 2
## 217 217.1 217.14 217.2 217.33 217.5 217.6 217.67 217.71 217.75 217.82
## 6 1 2 1 3 1 2 4 1 1 1
## 217.86 218 218.4 218.6 218.67 218.75 219 219.03 219.14 219.2 219.29
## 1 9 1 1 1 1 33 1 1 1 1
## 219.32 219.33 219.43 219.5 219.58 219.6 219.67 219.8 219.86 220 220.33
## 1 2 1 3 1 1 1 1 1 13 1
## 220.4 220.5 220.53 220.87 221 221.07 221.25 221.38 221.43 221.5 221.6
## 1 2 1 1 23 1 1 1 1 1 1
## 221.62 222 222.02 222.07 222.14 222.2 222.26 222.33 222.5 222.67 222.86
## 1 21 1 1 1 2 1 2 3 1 1
## 222.93 223 223.07 223.33 223.5 223.57 224 224.1 224.39 224.4 224.44
## 1 8 1 1 2 2 15 1 1 1 1
## 224.5 224.67 224.9 225 225.08 225.14 225.28 225.33 225.45 225.5 225.6
## 1 6 1 19 1 1 1 1 1 2 3
## 225.67 225.71 225.9 226 226.14 226.33 226.5 226.57 226.73 227 227.06
## 2 1 1 9 1 1 1 1 1 5 1
## 227.1 227.22 227.6 227.67 227.81 227.86 227.92 228 228.32 228.33 228.5
## 4 1 1 1 1 1 1 6 1 1 1
## 228.57 228.6 228.75 228.89 229 229.13 229.17 229.2 229.33 229.4 229.5
## 2 1 1 1 18 1 1 1 1 1 2
## 229.67 229.71 230 230.3 230.35 230.5 230.6 230.64 230.67 230.86 231
## 1 1 62 1 1 2 1 1 1 1 21
## 231.13 231.22 231.43 231.5 231.6 231.8 231.84 232 232.33 232.5 232.6
## 1 1 1 1 2 1 1 11 2 1 1
## 232.61 232.67 232.91 233 233.05 233.1 233.92 234 234.29 234.6 234.67
## 1 1 1 10 1 1 1 14 1 1 2
## 234.7 235 235.07 235.29 235.57 235.6 235.67 235.71 235.95 236 236.1
## 1 7 1 1 1 2 2 1 1 12 1
## 236.14 236.5 236.67 236.71 236.86 237 237.33 237.34 237.5 237.71 237.9
## 1 2 2 1 1 12 2 1 3 1 1
## 238 238.1 238.14 238.3 238.5 238.57 238.63 238.75 239 239.3 239.43
## 7 1 1 1 1 2 1 1 19 1 1
## 239.5 239.65 239.66 239.67 240 240.5 240.6 241 241.1 241.5 241.6
## 1 1 1 1 17 1 2 19 1 2 1
## 241.67 241.75 242 242.1 242.25 242.32 242.33 242.57 242.6 242.67 242.75
## 1 1 9 1 1 1 2 1 1 1 1
## 242.84 243 243.16 243.17 243.33 243.5 243.67 243.71 243.8 243.81 244
## 2 6 1 1 1 1 1 1 1 1 17
## 244.09 244.31 244.55 244.72 245 245.1 245.2 245.3 245.33 246 246.02
## 1 1 1 1 9 1 1 1 4 13 1
## 246.2 246.43 246.5 246.8 247 247.09 247.2 247.33 247.57 247.67 248
## 1 1 1 1 5 1 1 2 1 1 8
## 248.1 248.16 248.29 248.36 248.5 248.8 249 249.23 249.38 249.5 249.67
## 1 1 2 1 2 1 12 1 1 2 1
## 250 250.32 250.33 250.5 251 251.14 251.43 251.5 251.6 251.73 252
## 17 1 1 1 10 1 1 1 1 1 15
## 252.3 252.33 252.67 253 253.25 253.33 253.49 253.5 253.6 253.65 253.8
## 1 1 1 7 2 1 1 1 1 1 2
## 254 254.1 254.62 254.67 255 255.45 255.84 256 256.5 256.75 257
## 8 1 1 1 6 2 1 6 1 1 5
## 257.34 257.5 257.6 257.67 257.75 258 258.33 258.43 259 259.33 260
## 1 1 2 1 2 7 1 1 19 1 8
## 260.5 260.6 260.71 261 261.33 261.4 261.5 262 262.5 262.6 263
## 1 1 1 9 1 1 1 15 1 1 4
## 263.57 264 264.5 265 265.25 266 266.06 266.3 266.4 266.5 266.6
## 1 8 1 7 1 7 1 1 1 2 2
## 267 267.5 267.86 268 268.3 268.5 269 270 270.38 270.5 270.51
## 2 1 1 5 1 2 9 10 1 1 1
## 270.71 271 272 272.5 272.7 273 273.09 273.25 273.5 274 274.45
## 1 13 3 1 1 2 1 1 1 6 1
## 274.5 274.67 275 275.25 275.33 275.6 276 276.43 276.46 276.6 276.83
## 1 1 6 1 1 1 3 1 1 2 1
## 277 277.5 277.71 278 278.14 278.5 278.57 279 279.33 279.36 279.5
## 5 1 1 1 1 1 1 3 1 1 1
## 280 280.33 281 282 282.03 282.32 283.32 283.5 283.6 284 284.29
## 1 1 5 5 1 1 1 1 1 4 1
## 284.7 284.86 285 286 286.1 286.25 286.6 286.79 287 288 288.1
## 1 2 1 3 1 1 1 1 2 2 1
## 289 289.5 289.6 289.8 290 290.67 291 292 292.5 292.71 293
## 4 1 1 2 2 1 2 8 1 1 2
## 293.1 293.33 294 294.29 294.5 294.86 295 295.5 295.67 296 296.33
## 1 1 3 1 2 1 3 1 2 1 1
## 296.5 296.75 297 297.29 297.57 298 298.71 299 299.2 299.33 300
## 1 1 4 1 1 5 1 6 1 1 2
## 300.4 300.6 300.86 301.43 302 302.11 302.67 302.86 303 303.7 304
## 2 1 1 1 1 1 1 1 2 1 2
## 305 306 306.5 306.7 307 307.5 307.67 308 308.4 308.57 309
## 10 4 1 1 1 1 1 3 1 2 1
## 310 311 311.25 311.6 311.7 312 313.71 314 314.5 315 315.33
## 5 3 1 1 1 1 1 1 1 5 1
## 315.71 316.5 317 317.67 318 318.82 319 320.7 322 323 324
## 1 1 3 1 2 1 1 1 4 1 1
## 325 326 326.71 327.4 328 328.33 329 330 330.57 331 331.43
## 4 1 1 1 3 1 3 2 1 2 1
## 332 335 336.57 337 338 339.4 340 340.71 343 344.67 350.2
## 2 2 1 1 2 1 4 1 2 1 1
## 350.75 351 352 353 353.67 357 358.75 359 360 363 367
## 1 1 1 1 1 2 1 1 1 1 2
## 369 372.71 377 378 382 388 392 402 426.25 437 450
## 1 1 1 2 1 2 1 1 1 1 1required_car_parking_spaces
As for parking spaces, most of the bookings include 0 parking space, however the ones that do seem to have no cancellations proportially. Indicatint hat when people request a parking space, they no not (easily) cancel their booking.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = required_car_parking_spaces))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = required_car_parking_spaces,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per required_car_parking_spaces (conditional probability)
df_required_car_parking_spaces <- bookings_train %>%
group_by(is_cancelled, required_car_parking_spaces) %>%
summarize(n = n()) %>%
group_by(required_car_parking_spaces) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_required_car_parking_spaces, aes(x = required_car_parking_spaces, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$required_car_parking_spaces))## [1] 0# Check for bizarre values
sort(unique(bookings_train$required_car_parking_spaces))## [1] 0 1 2 3# Unique vals + count
sort(table(bookings_train$required_car_parking_spaces))##
## 3 2 1 0
## 1 19 3272 20743total_of_special_requests
It can be observed within the graphs that most people do not have any special guests, but there seems to be a pattern in which the more special guests that are included in the booking, the higher the chance of non-cancellation.
ggplot(data = bookings_train) +
geom_bar(mapping = aes(x = total_of_special_requests))
# Visualisation of cancelled per weeknumber
ggplot(bookings_train,
aes(x = total_of_special_requests,
fill = is_cancelled)) +
geom_bar(position = "dodge")
# Visualisation of cancelled per total_of_special_requests (conditional probability)
df_total_of_special_requests <- bookings_train %>%
group_by(is_cancelled, total_of_special_requests) %>%
summarize(n = n()) %>%
group_by(total_of_special_requests) %>%
mutate(prob = n / sum(n))## `summarise()` has grouped output by 'is_cancelled'. You can override using the
## `.groups` argument.ggplot(df_total_of_special_requests, aes(x = total_of_special_requests, y = prob, fill = is_cancelled)) +
geom_col() 
# Check missing values
sum(is.na(bookings_train$total_of_special_requests))## [1] 0# Check for bizarre values
sort(unique(bookings_train$total_of_special_requests))## [1] 0 1 2 3 4 5# Unique vals + count
sort(table(bookings_train$total_of_special_requests))##
## 5 4 3 2 1 0
## 8 83 539 2904 7052 13449